dEjournals Nyitóoldal
International Journal of Engineering and Management Sciences
Regisztráció Aktuális szám Megjelenés előtti cikkek Archívum Indexáltság Beküldés Kapcsolat Keresés
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IJEMS

Évf. 8 szám 3 (2023)
Gépészeti és járműmérnöki tudományok

Manufacturing Process Optimization and Tool Condition Monitoring in Mechanical Engineering

Megjelent:
2023-09-29
DOI Linkhttps://doi.org/10.21791/IJEMS.2023.026
Szerzők
Deák Krisztián,
Menyhárt József
Megtekintés
PDF (Angol)
Kulcsszavak
Tecnomatix plant simulation tool, wear diagnosis machine learning
Licenc

Copyright (c) 2023 Krisztián Deák, József Menyhárt

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Hogyan hivatkozzuk
Kiválasztott formátum: APA
Deák, K., & Menyhárt, J. (2023). Manufacturing Process Optimization and Tool Condition Monitoring in Mechanical Engineering. International Journal of Engineering and Management Sciences, 8(3), 72-89. https://doi.org/10.21791/IJEMS.2023.026
Beküldött 2023-01-21
Elfogadott 2023-03-20
Publikált 2023-09-29
Absztrakt

The optimization of manufacturing and production processes with various computer software is essential these days. Solutions on the market allow us to optimize and improve our manufacturing and production processes; one of the most popular software is called Tecnomatrix, which is described in this paper. Tool condition monitoring is a vital part of the manufacturing process in the industry. It requires continuous measurement of the wear of the cutting tool edges to improve the surface quality of the work piece and maintain productivity. Multiple methods are available for the determination of the actual condition of the cutting tool. Vibration diagnostics and acoustic methods are included in this paper. These methods are simple, it requires only high sensitive sensors, microphones, and data acquisition unit to gather the vibration signal and make signal improvement. Extended Taylor equation is applied for tool edge wear ratio. Labview and Matlab software are applied for the measurement and the digital signal processing. Machine learning method with artificial neural network is for the detection and prediction of the edge wear to estimate the remaining useful lifetime (RUL) of the tool.

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